Cross-cultural adaptation and validation of a French version of the Measure of Stroke Environment (MOSE) in stroke survivors in Sub-Saharan Africa
Bibliographic record
Abstract
Purpose To investigate the psychometric properties of the MOSE-Benin, a French-language version of the Measure of Stroke Environment (MOSE) for Sub-Saharan Africa.Materials and methods The original English version of the MOSE has been translated into French following the guidelines for cross-cultural adaptation. The resulting questionnaire (MOSE-Benin) was administered to a convenience sample of participants recruited in Benin, a French-speaking country.Results Eighty-two stroke survivors (41 females; mean ± SD: 54.94 ± 11.6 years old) participated in the study. Internal consistency of each domain of the MOSE-Benin and the overall questionnaire was high (Cronbach’s α: 0.78 to 0.92). Test-retest reliability was excellent (n = 31; ICC: 0.977 to 0.998). Overall, the standard error of measurement (SEM) and the minimum detectable change (MDC) showed very low values (SEM = 0.85; MDC = 2.35). Convergent validity demonstrated moderate correlations for the three domains in separate comparison respectively with the ACTIVLIM-Stroke questionnaire, the Participation Measurement Scale, and the communication domain of the Stroke Impact Scale (r or ρ: 0.42 to 0.54; p < 0.0001).Conclusion MOSE-Benin has good evidence regarding psychometric properties (i.e., content validity, convergent validity, internal consistency, and test-retest reliability) that can support its use for the assessment of perceived environmental barriers after stroke in a French-speaking Sub-Saharan African country, such as Benin.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".